Dataset Information

Closed-loop Neuropharmacology for Epilepsy: Distant Dream or Future Reality?

ABSTRACT: Epilepsy is considered the most frequent severe neurological condition but most patients treated with medication become seizure free. The management of treatment, however, is highly empirical, mainly relying on observation. A closed-loop therapy for epilepsy would be very valuable for more efficient treatment regimens. Here we discuss monitoring treatment (therapeutic drug monitoring) and the potential developments in this field, as well as providing a review of potential biomarkers that could be used to monitor the disease activity. Finally, we consider the pharmacogenetic input in epilepsy treatment.

SUBMITTER: Aicua-Rapun I

PROVIDER: S-EPMC6520584 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Closed-loop Neuropharmacology for Epilepsy: Distant Dream or Future Reality?

Aicua-Rapun Irene I André Pascal P Novy Jan J

Current neuropharmacology 20190101 5

Epilepsy is considered the most frequent severe neurological condition but most patients treated with medication become seizure free. The management of treatment, however, is highly empirical, mainly relying on observation. A closed-loop therapy for epilepsy would be very valuable for more efficient treatment regimens. Here we discuss monitoring treatment (therapeutic drug monitoring) and the potential developments in this field, as well as providing a review of potential biomarkers that could b ...[more]

PMID: 29521237

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Project description:Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.

Dataset Information

Closed-loop Neuropharmacology for Epilepsy: Distant Dream or Future Reality?

Publications

Closed-loop Neuropharmacology for Epilepsy: Distant Dream or Future Reality?

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